saujs Sakarya University Journal of Science 2147-835X Sakarya University 10.16984/saufenbilder.886583 Software Engineering (Other) Engineering Yazılım Mühendisliği (Diğer) Mühendislik Sentiment Classification Performance Analysis Based on Glove Word Embedding https://orcid.org/0000-0002-3605-8621 Kırelli Yasin İSTİNYE ÜNİVERSİTESİ https://orcid.org/0000-0001-6668-6285 Özdemir Şebnem İSTİNYE ÜNİVERSİTESİ 06 30 2021 25 3 639 646 02 25 2021 04 02 2021 Copyright © 1997, Sakarya University Journal of Science 1997 Sakarya University Journal of Science

Representation of words in mathematical expressions is an essential issue in natural language processing. In this study, data sets in different categories are classified as positive or negative according to their content. Using the Glove (Global Vector for Word Representation) method, which is one of the word embedding methods, the effect of the vector set based on the word similarities previously calculated on the classification performance has been analyzed. In this study, the effect of pretrained, embedded and deterministic word embedding classification performance has analyzed by using Long Short Term Memory (LSTM). The porposed LSTM based deep learning model has been tested on three different data sets and the results was evaluated.

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